MétaCan
Menu
Back to cohort
Record W3216716392 · doi:10.1109/mper.2002.4312526

Fast Ferroresonance Suppression of Coupling Capacitor Voltage Transformers

2002· article· en· W3216716392 on OpenAlexaff
M. Graovac, Mohammad Reza Iravani, Xiaolin Wang, R. D. McTaggart

Bibliographic record

VenueIEEE Power Engineering Review · 2002
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFerroresonance in electricity networksVaristorOvervoltageTransformerEmtpClearanceCapacitorTransient (computer programming)Control theory (sociology)Electrical engineeringVoltageElectronic engineeringEngineeringComputer sciencePhysicsElectric power system

Abstract

fetched live from OpenAlex

This paper describes a procedure for fast suppression of the phenomenon of ferroresosnance in coupling capacitor voltage transformers (CCVT) without major change in the CCVT design. It is possible to adjust parameters of the secondary overvoltage protection and the filter circuit so that the ferroresonance can be cleared in a very short time interval. Study cases show that ferroresonance is effectively cleared within two cycles. An implementation of metal oxide varistors (MOV) as part of passive ferroresonance protection is also addressed. The electromagnetic transients program (EMTP) is used for modeling transients and fine-tuning the ferroresonance suppressing circuit. The studies are conducted on the Trench TEHMP161A GOVT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.218
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Power Engineering ReviewSame topicMagnetic Properties and ApplicationsFrench-language works237,207